Self-Growing Spatial Graph Networks for Pedestrian Trajectory Prediction

Sirin Haddad, Siew-Kei Lam; The IEEE Winter Conference on Applications of Computer Vision (WACV), 2020, pp. 1151-1159

Abstract


Intelligent vehicles and social robots need to navigate in crowded environments while avoiding collisions with pedestrians. To achieve this, pedestrian trajectory prediction is essential. However, predicting pedestrians' trajectory in crowded environments is nontrivial as human-to-human interactions among the crowd participants influence their motion. In this work, we propose a novel end-to-end graph-centric gated learning model to estimate the existence of interactions between individuals. Accordingly, the model predicts pedestrians' future locations and velocities. Recent methods based on LSTM networks used thresholding techniques to define neighborhood boundaries and relationships. Other graph-structured methods grow edges in polynomial size. In contrast, our graph-based GRU network model employs an online data-driven criterion that can learn from interactions and grow connections between pedestrian nodes. The proposed model yields outperforming prediction accuracy over state-of-the-art works in two public datasets, i.e. Crowds and SDD.

Related Material


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[bibtex]
@InProceedings{Haddad_2020_WACV,
author = {Haddad, Sirin and Lam, Siew-Kei},
title = {Self-Growing Spatial Graph Networks for Pedestrian Trajectory Prediction},
booktitle = {The IEEE Winter Conference on Applications of Computer Vision (WACV)},
month = {March},
year = {2020}
}